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system</title> <meta name="description" content="Search results for: sparse system"> <meta name="keywords" content="sparse system"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="sparse system"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 8478</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: sparse system</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8478</span> A Transform Domain Function Controlled VSSLMS Algorithm for Sparse System Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Cemil%20Turan">Cemil Turan</a>, <a href="https://publications.waset.org/search?q=Mohammad%20Shukri%20Salman"> Mohammad Shukri Salman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Adaptive%20filtering" title="Adaptive filtering">Adaptive filtering</a>, <a href="https://publications.waset.org/search?q=sparse%20system%20identification" title=" sparse system identification"> sparse system identification</a>, <a href="https://publications.waset.org/search?q=VSSLMS%20algorithm" title=" VSSLMS algorithm"> VSSLMS algorithm</a>, <a href="https://publications.waset.org/search?q=TD-LMS%20algorithm." title=" TD-LMS algorithm."> TD-LMS algorithm.</a> </p> <a href="https://publications.waset.org/10007462/a-transform-domain-function-controlled-vsslms-algorithm-for-sparse-system-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10007462/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10007462/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10007462/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10007462/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10007462/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10007462/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10007462/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10007462/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10007462/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10007462/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10007462.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1000</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8477</span> Sparse Frequencies Extracting from Partial Phase-Only Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.%20Fan">R. Fan</a>, <a href="https://publications.waset.org/search?q=Q.%20Wan"> Q. Wan</a>, <a href="https://publications.waset.org/search?q=H.%20Chen"> H. Chen</a>, <a href="https://publications.waset.org/search?q=Y.L.%20Liu"> Y.L. Liu</a>, <a href="https://publications.waset.org/search?q=Y.P.%20Liu"> Y.P. Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper considers a robust recovery of sparse frequencies from partial phase-only measurements. With the proposed method, sparse frequencies can be reconstructed, which makes full use of the sparse distribution in the Fourier representation of the complex-valued time signal. Simulation experiments illustrate the proposed method-s advantages over conventional methods in both noiseless and additive white Gaussian noise cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Sparse%20signal%20recovery" title="Sparse signal recovery">Sparse signal recovery</a>, <a href="https://publications.waset.org/search?q=phase-only%20measurements" title=" phase-only measurements"> phase-only measurements</a>, <a href="https://publications.waset.org/search?q=Compressive%20sensing" title="Compressive sensing">Compressive sensing</a>, <a href="https://publications.waset.org/search?q=convex%20relaxation." title=" convex relaxation."> convex relaxation.</a> </p> <a href="https://publications.waset.org/1128/sparse-frequencies-extracting-from-partial-phase-only-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1128/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1128/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1128/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1128/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1128/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1128/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1128/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1128/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1128/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1128/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1128.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1466</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8476</span> Sparsity-Aware and Noise-Robust Subband Adaptive Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Young-Seok%20Choi">Young-Seok Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a subband adaptive filter (SAF) for a system identification where an impulse response is sparse and disturbed with an impulsive noise. Benefiting from the uses of l1-norm optimization and l0-norm penalty of the weight vector in the cost function, the proposed l0-norm sign SAF (l0-SSAF) achieves both robustness against impulsive noise and much improved convergence behavior than the classical adaptive filters. Simulation results in the system identification scenario confirm that the proposed l0-norm SSAF is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Subband%20adaptive%20filter" title="Subband adaptive filter">Subband adaptive filter</a>, <a href="https://publications.waset.org/search?q=l0-norm" title=" l0-norm"> l0-norm</a>, <a href="https://publications.waset.org/search?q=sparse%20system" title=" sparse system"> sparse system</a>, <a href="https://publications.waset.org/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/search?q=impulsive%20interference." title=" impulsive interference."> impulsive interference.</a> </p> <a href="https://publications.waset.org/10003881/sparsity-aware-and-noise-robust-subband-adaptive-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003881/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003881/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003881/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003881/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003881/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003881/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003881/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003881/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003881/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003881/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003881.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1790</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8475</span> An Improved Method to Compute Sparse Graphs for Traveling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Y.%20Wang">Y. Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The Traveling salesman problem (TSP) is NP-hard in combinatorial optimization. The research shows the algorithms for TSP on the sparse graphs have the shorter computation time than those for TSP according to the complete graphs. We present an improved iterative algorithm to compute the sparse graphs for TSP by frequency graphs computed with frequency quadrilaterals. The iterative algorithm is enhanced by adjusting two parameters of the algorithm. The computation time of the algorithm is <em>O</em>(<em>CN</em><sub>max</sub><em>n</em><sup>2</sup>) where <em>C</em> is the iterations, <em>N</em><sub>max</sub> is the maximum number of frequency quadrilaterals containing each edge and <em>n</em> is the scale of TSP. The experimental results showed the computed sparse graphs generally have less than 5<em>n</em> edges for most of these Euclidean instances. Moreover, the maximum degree and minimum degree of the vertices in the sparse graphs do not have much difference. Thus, the computation time of the methods to resolve the TSP on these sparse graphs will be greatly reduced.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Frequency%20quadrilateral" title="Frequency quadrilateral">Frequency quadrilateral</a>, <a href="https://publications.waset.org/search?q=iterative%20algorithm" title=" iterative algorithm"> iterative algorithm</a>, <a href="https://publications.waset.org/search?q=sparse%20graph" title=" sparse graph"> sparse graph</a>, <a href="https://publications.waset.org/search?q=traveling%20salesman%20problem." title=" traveling salesman problem. "> traveling salesman problem. </a> </p> <a href="https://publications.waset.org/10008525/an-improved-method-to-compute-sparse-graphs-for-traveling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10008525/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10008525/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10008525/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10008525/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10008525/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10008525/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10008525/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10008525/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10008525/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10008525/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10008525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1010</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8474</span> Finding Sparse Features in Face Detection Using Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=H.%20Sagha">H. Sagha</a>, <a href="https://publications.waset.org/search?q=S.%20Kasaei"> S. Kasaei</a>, <a href="https://publications.waset.org/search?q=E.%20Enayati"> E. Enayati</a>, <a href="https://publications.waset.org/search?q=M.%20Dehghani"> M. Dehghani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although Face detection is not a recent activity in the field of image processing, it is still an open area for research. The greatest step in this field is the work reported by Viola and its recent analogous is Huang et al. Both of them use similar features and also similar training process. The former is just for detecting upright faces, but the latter can detect multi-view faces in still grayscale images using new features called 'sparse feature'. Finding these features is very time consuming and inefficient by proposed methods. Here, we propose a new approach for finding sparse features using a genetic algorithm system. This method requires less computational cost and gets more effective features in learning process for face detection that causes more accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Face%20Detection" title="Face Detection">Face Detection</a>, <a href="https://publications.waset.org/search?q=Genetic%20Algorithms" title=" Genetic Algorithms"> Genetic Algorithms</a>, <a href="https://publications.waset.org/search?q=Sparse%0AFeature." title=" Sparse Feature."> Sparse Feature.</a> </p> <a href="https://publications.waset.org/3993/finding-sparse-features-in-face-detection-using-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3993/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3993/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3993/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3993/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3993/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3993/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3993/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3993/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3993/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3993/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3993.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1581</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8473</span> Learning an Overcomplete Dictionary using a Cauchy Mixture Model for Sparse Decay</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=E.%20S.%20Gower">E. S. Gower</a>, <a href="https://publications.waset.org/search?q=M.%20O.%20J.%20Hawksford"> M. O. J. Hawksford</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An algorithm for learning an overcomplete dictionary using a Cauchy mixture model for sparse decomposition of an underdetermined mixing system is introduced. The mixture density function is derived from a ratio sample of the observed mixture signals where 1) there are at least two but not necessarily more mixture signals observed, 2) the source signals are statistically independent and 3) the sources are sparse. The basis vectors of the dictionary are learned via the optimization of the location parameters of the Cauchy mixture components, which is shown to be more accurate and robust than the conventional data mining methods usually employed for this task. Using a well known sparse decomposition algorithm, we extract three speech signals from two mixtures based on the estimated dictionary. Further tests with additive Gaussian noise are used to demonstrate the proposed algorithm-s robustness to outliers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=expectation-maximization" title="expectation-maximization">expectation-maximization</a>, <a href="https://publications.waset.org/search?q=Pitman%20estimator" title=" Pitman estimator"> Pitman estimator</a>, <a href="https://publications.waset.org/search?q=sparsedecomposition" title=" sparsedecomposition"> sparsedecomposition</a> </p> <a href="https://publications.waset.org/10330/learning-an-overcomplete-dictionary-using-a-cauchy-mixture-model-for-sparse-decay" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10330/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10330/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10330/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10330/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10330/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10330/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10330/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10330/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10330/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10330/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10330.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1949</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8472</span> Sparsity-Aware Affine Projection Algorithm for System Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Young-Seok%20Choi">Young-Seok Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a new type of the affine projection (AP) algorithms which incorporate the sparsity condition of a system. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weighting for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results exhibit that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=System%20identification" title="System identification">System identification</a>, <a href="https://publications.waset.org/search?q=adaptive%20filter" title=" adaptive filter"> adaptive filter</a>, <a href="https://publications.waset.org/search?q=affine%20projection" title=" affine projection"> affine projection</a>, <a href="https://publications.waset.org/search?q=sparsity" title=" sparsity"> sparsity</a>, <a href="https://publications.waset.org/search?q=sparse%20system." title=" sparse system."> sparse system.</a> </p> <a href="https://publications.waset.org/10003838/sparsity-aware-affine-projection-algorithm-for-system-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003838/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003838/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003838/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003838/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003838/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003838/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003838/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003838/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003838/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003838/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003838.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1555</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8471</span> Gene Expression Data Classification Using Discriminatively Regularized Sparse Subspace Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Chunming%20Xu">Chunming Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse representation which can represent high dimensional data effectively has been successfully used in computer vision and pattern recognition problems. However, it doesn-t consider the label information of data samples. To overcome this limitation, we develop a novel dimensionality reduction algorithm namely dscriminatively regularized sparse subspace learning(DR-SSL) in this paper. The proposed DR-SSL algorithm can not only make use of the sparse representation to model the data, but also can effective employ the label information to guide the procedure of dimensionality reduction. In addition,the presented algorithm can effectively deal with the out-of-sample problem.The experiments on gene-expression data sets show that the proposed algorithm is an effective tool for dimensionality reduction and gene-expression data classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=sparse%20representation" title="sparse representation">sparse representation</a>, <a href="https://publications.waset.org/search?q=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a>, <a href="https://publications.waset.org/search?q=labelinformation" title=" labelinformation"> labelinformation</a>, <a href="https://publications.waset.org/search?q=sparse%20subspace%20learning" title=" sparse subspace learning"> sparse subspace learning</a>, <a href="https://publications.waset.org/search?q=gene-expression%20data%20classification." title=" gene-expression data classification."> gene-expression data classification.</a> </p> <a href="https://publications.waset.org/7397/gene-expression-data-classification-using-discriminatively-regularized-sparse-subspace-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7397/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7397/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7397/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7397/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7397/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7397/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7397/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7397/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7397/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7397/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7397.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1447</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8470</span> Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ali%20Pour%20Yazdanpanah">Ali Pour Yazdanpanah</a>, <a href="https://publications.waset.org/search?q=Farideh%20Foroozandeh%20Shahraki"> Farideh Foroozandeh Shahraki</a>, <a href="https://publications.waset.org/search?q=Emma%20Regentova"> Emma Regentova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited to improve the reconstruction quality in sparse-view CT, and the convex constraint in those problems leads to an easy optimization process. However, convex regularizers often result in a biased approximation and inaccurate reconstruction in CT problems. Here, we present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction. We compare our method with previously reported high performance methods which use convex regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV) on the test phantom images. The results show that there are benefits in using the nonconvex regularizer in the sparse-view CT reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Computed%20tomography" title="Computed tomography">Computed tomography</a>, <a href="https://publications.waset.org/search?q=sparse-view%20reconstruction" title=" sparse-view reconstruction"> sparse-view reconstruction</a>, <a href="https://publications.waset.org/search?q=L1%20%E2%88%92L2%20minimization" title=" L1 −L2 minimization"> L1 −L2 minimization</a>, <a href="https://publications.waset.org/search?q=non-convex" title=" non-convex"> non-convex</a>, <a href="https://publications.waset.org/search?q=difference%20of%20convex%20functions." title=" difference of convex functions."> difference of convex functions.</a> </p> <a href="https://publications.waset.org/10006930/sparse-view-ct-reconstruction-based-on-nonconvex-l1-l2-regularizations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10006930/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10006930/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10006930/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10006930/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10006930/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10006930/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10006930/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10006930/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10006930/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10006930/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10006930.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2033</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8469</span> A Dictionary Learning Method Based On EMD for Audio Sparse Representation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yueming%20Wang">Yueming Wang</a>, <a href="https://publications.waset.org/search?q=Zenghui%20Zhang"> Zenghui Zhang</a>, <a href="https://publications.waset.org/search?q=Rendong%20Ying"> Rendong Ying</a>, <a href="https://publications.waset.org/search?q=Peilin%20Liu"> Peilin Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Sparse representation has long been studied and several dictionary learning methods have been proposed. The dictionary learning methods are widely used because they are adaptive. In this paper, a new dictionary learning method for audio is proposed. Signals are at first decomposed into different degrees of Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD) technique. Then these IMFs form a learned dictionary. To reduce the size of the dictionary, the K-means method is applied to the dictionary to generate a K-EMD dictionary. Compared to K-SVD algorithm, the K-EMD dictionary decomposes audio signals into structured components, thus the sparsity of the representation is increased by 34.4% and the SNR of the recovered audio signals is increased by 20.9%.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Dictionary%20Learning" title="Dictionary Learning">Dictionary Learning</a>, <a href="https://publications.waset.org/search?q=EMD" title=" EMD"> EMD</a>, <a href="https://publications.waset.org/search?q=K-means%20Method" title=" K-means Method"> K-means Method</a>, <a href="https://publications.waset.org/search?q=Sparse%0D%0ARepresentation." title=" Sparse Representation."> Sparse Representation.</a> </p> <a href="https://publications.waset.org/16628/a-dictionary-learning-method-based-on-emd-for-audio-sparse-representation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16628/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16628/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16628/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16628/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16628/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16628/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16628/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16628/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16628/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16628/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16628.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2628</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8468</span> Compressed Sensing of Fetal Electrocardiogram Signals Based on Joint Block Multi-Orthogonal Least Squares Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Xiang%20Jianhong">Xiang Jianhong</a>, <a href="https://publications.waset.org/search?q=Wang%20Cong"> Wang Cong</a>, <a href="https://publications.waset.org/search?q=Wang%20Linyu"> Wang Linyu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=telemedicine" title="telemedicine">telemedicine</a>, <a href="https://publications.waset.org/search?q=fetal%20electrocardiogram" title=" fetal electrocardiogram"> fetal electrocardiogram</a>, <a href="https://publications.waset.org/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a>, <a href="https://publications.waset.org/search?q=joint%20sparse%20reconstruction" title=" joint sparse reconstruction"> joint sparse reconstruction</a>, <a href="https://publications.waset.org/search?q=block%20sparse%20signal" title=" block sparse signal"> block sparse signal</a> </p> <a href="https://publications.waset.org/10013140/compressed-sensing-of-fetal-electrocardiogram-signals-based-on-joint-block-multi-orthogonal-least-squares-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013140/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013140/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013140/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013140/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013140/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013140/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013140/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013140/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013140/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013140/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013140.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">511</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8467</span> Model-Free Distributed Control of Dynamical Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Javad%20Khazaei">Javad Khazaei</a>, <a href="https://publications.waset.org/search?q=Rick%20S.%20Blum"> Rick S. Blum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Distributed control is an efficient and flexible approach for coordination of multi-agent systems. One of the main challenges in designing a distributed controller is identifying the governing dynamics of the dynamical systems. Data-driven system identification is currently undergoing a revolution. With the availability of high-fidelity measurements and historical data, model-free identification of dynamical systems can facilitate the control design without tedious modeling of high-dimensional and/or nonlinear systems. This paper develops a distributed control design using consensus theory for linear and nonlinear dynamical systems using sparse identification of system dynamics. Compared with existing consensus designs that heavily rely on knowing the detailed system dynamics, the proposed model-free design can accurately capture the dynamics of the system with available measurements and input data and provide guaranteed performance in consensus and tracking problems. Heterogeneous damped oscillators are chosen as examples of dynamical system for validation purposes.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Consensus%20tracking" title="Consensus tracking">Consensus tracking</a>, <a href="https://publications.waset.org/search?q=distributed%20control" title=" distributed control"> distributed control</a>, <a href="https://publications.waset.org/search?q=model-free%0D%0Acontrol" title=" model-free control"> model-free control</a>, <a href="https://publications.waset.org/search?q=sparse%20identification%20of%20dynamical%20systems." title=" sparse identification of dynamical systems."> sparse identification of dynamical systems.</a> </p> <a href="https://publications.waset.org/10012723/model-free-distributed-control-of-dynamical-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012723/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012723/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012723/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012723/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012723/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012723/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012723/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012723/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012723/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012723/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">535</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8466</span> A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Pan%20Long">Pan Long</a>, <a href="https://publications.waset.org/search?q=Bi%20Dongjie"> Bi Dongjie</a>, <a href="https://publications.waset.org/search?q=Li%20Xifeng"> Li Xifeng</a>, <a href="https://publications.waset.org/search?q=Xie%20Yongle"> Xie Yongle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Complex-valued%20signal%20processing" title="Complex-valued signal processing">Complex-valued signal processing</a>, <a href="https://publications.waset.org/search?q=synthetic%20aperture%0D%0Aradar%20%28SAR%29" title=" synthetic aperture radar (SAR)"> synthetic aperture radar (SAR)</a>, <a href="https://publications.waset.org/search?q=2-D%20radar%20imaging" title=" 2-D radar imaging"> 2-D radar imaging</a>, <a href="https://publications.waset.org/search?q=compressive%20sensing" title=" compressive sensing"> compressive sensing</a>, <a href="https://publications.waset.org/search?q=Sparse%0D%0ABayesian%20learning." title=" Sparse Bayesian learning."> Sparse Bayesian learning.</a> </p> <a href="https://publications.waset.org/10010396/a-generalized-sparse-bayesian-learning-algorithm-for-near-field-synthetic-aperture-radar-imaging-by-exploiting-impropriety-and-noncircularity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10010396/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10010396/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10010396/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10010396/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10010396/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10010396/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10010396/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10010396/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10010396/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10010396/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10010396.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1526</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8465</span> The Classification Model for Hard Disk Drive Functional Tests under Sparse Data Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Pattanapairoj">S. Pattanapairoj</a>, <a href="https://publications.waset.org/search?q=D.%20Chetchotsak"> D. Chetchotsak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposed classification models that would be used as a proxy for hard disk drive (HDD) functional test equitant which required approximately more than two weeks to perform the HDD status classification in either “Pass" or “Fail". These models were constructed by using committee network which consisted of a number of single neural networks. This paper also included the method to solve the problem of sparseness data in failed part, which was called “enforce learning method". Our results reveal that the constructed classification models with the proposed method could perform well in the sparse data conditions and thus the models, which used a few seconds for HDD classification, could be used to substitute the HDD functional tests. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Sparse%20data" title="Sparse data">Sparse data</a>, <a href="https://publications.waset.org/search?q=Classifications" title=" Classifications"> Classifications</a>, <a href="https://publications.waset.org/search?q=Committee%20network" title=" Committee network"> Committee network</a> </p> <a href="https://publications.waset.org/15589/the-classification-model-for-hard-disk-drive-functional-tests-under-sparse-data-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15589/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15589/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15589/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15589/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15589/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15589/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15589/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15589/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15589/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15589/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15589.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1736</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8464</span> A Sparse Representation Speech Denoising Method Based on Adapted Stopping Residue Error</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Qianhua%20He">Qianhua He</a>, <a href="https://publications.waset.org/search?q=Weili%20Zhou"> Weili Zhou</a>, <a href="https://publications.waset.org/search?q=Aiwu%20Chen"> Aiwu Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Speech%20denoising" title="Speech denoising">Speech denoising</a>, <a href="https://publications.waset.org/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a>, <a href="https://publications.waset.org/search?q=K-singular%20value%20decomposition" title=" K-singular value decomposition"> K-singular value decomposition</a>, <a href="https://publications.waset.org/search?q=orthogonal%20matching%20pursuit." title=" orthogonal matching pursuit."> orthogonal matching pursuit.</a> </p> <a href="https://publications.waset.org/10008151/a-sparse-representation-speech-denoising-method-based-on-adapted-stopping-residue-error" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10008151/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10008151/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10008151/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10008151/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10008151/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10008151/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10008151/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10008151/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10008151/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10008151/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10008151.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1014</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8463</span> An Algorithm for Computing the Analytic Singular Value Decomposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Drahoslava%20Janovska">Drahoslava Janovska</a>, <a href="https://publications.waset.org/search?q=Vladimir%20Janovsky"> Vladimir Janovsky</a>, <a href="https://publications.waset.org/search?q=Kunio%20Tanabe"> Kunio Tanabe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A proof of convergence of a new continuation algorithm for computing the Analytic SVD for a large sparse parameter– dependent matrix is given. The algorithm itself was developed and numerically tested in [5].</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Analytic%20Singular%20Value%20Decomposition" title="Analytic Singular Value Decomposition">Analytic Singular Value Decomposition</a>, <a href="https://publications.waset.org/search?q=large%20sparse%0D%0Aparameter%E2%80%93dependent%20matrices" title=" large sparse parameter–dependent matrices"> large sparse parameter–dependent matrices</a>, <a href="https://publications.waset.org/search?q=continuation%20algorithm%20of%20a%20predictorcorrector%0D%0Atype." title=" continuation algorithm of a predictorcorrector type."> continuation algorithm of a predictorcorrector type.</a> </p> <a href="https://publications.waset.org/7617/an-algorithm-for-computing-the-analytic-singular-value-decomposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7617/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7617/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7617/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7617/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7617/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7617/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7617/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7617/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7617/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7617/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7617.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1456</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8462</span> A Patricia-Tree Approach for Frequent Closed Itemsets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Moez%20Ben%20Hadj%20Hamida">Moez Ben Hadj Hamida</a>, <a href="https://publications.waset.org/search?q=Yahya%20SlimaniI"> Yahya SlimaniI</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, we propose an adaptation of the Patricia-Tree for sparse datasets to generate non redundant rule associations. Using this adaptation, we can generate frequent closed itemsets that are more compact than frequent itemsets used in Apriori approach. This adaptation has been experimented on a set of datasets benchmarks.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Datamining" title="Datamining">Datamining</a>, <a href="https://publications.waset.org/search?q=Frequent%20itemsets" title=" Frequent itemsets"> Frequent itemsets</a>, <a href="https://publications.waset.org/search?q=Frequent%20closeditemsets" title=" Frequent closeditemsets"> Frequent closeditemsets</a>, <a href="https://publications.waset.org/search?q=Sparse%20datasets." title=" Sparse datasets."> Sparse datasets.</a> </p> <a href="https://publications.waset.org/11709/a-patricia-tree-approach-for-frequent-closed-itemsets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11709/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11709/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11709/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11709/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11709/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11709/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11709/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11709/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11709/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11709/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11709.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1884</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8461</span> Sparse Unmixing of Hyperspectral Data by Exploiting Joint-Sparsity and Rank-Deficiency</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Fanqiang%20Kong">Fanqiang Kong</a>, <a href="https://publications.waset.org/search?q=Chending%20Bian"> Chending Bian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we exploit two assumed properties of the abundances of the observed signatures (endmembers) in order to reconstruct the abundances from hyperspectral data. Joint-sparsity is the first property of the abundances, which assumes the adjacent pixels can be expressed as different linear combinations of same materials. The second property is rank-deficiency where the number of endmembers participating in hyperspectral data is very small compared with the dimensionality of spectral library, which means that the abundances matrix of the endmembers is a low-rank matrix. These assumptions lead to an optimization problem for the sparse unmixing model that requires minimizing a combined <em>l<sub>2,p</sub>-</em>norm and nuclear norm. We propose a variable splitting and augmented Lagrangian algorithm to solve the optimization problem. Experimental evaluation carried out on synthetic and real hyperspectral data shows that the proposed method outperforms the state-of-the-art algorithms with a better spectral unmixing accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Hyperspectral%20unmixing" title="Hyperspectral unmixing">Hyperspectral unmixing</a>, <a href="https://publications.waset.org/search?q=joint-sparse" title=" joint-sparse"> joint-sparse</a>, <a href="https://publications.waset.org/search?q=low-rank%20representation" title=" low-rank representation"> low-rank representation</a>, <a href="https://publications.waset.org/search?q=abundance%20estimation." title=" abundance estimation. "> abundance estimation. </a> </p> <a href="https://publications.waset.org/10007535/sparse-unmixing-of-hyperspectral-data-by-exploiting-joint-sparsity-and-rank-deficiency" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10007535/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10007535/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10007535/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10007535/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10007535/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10007535/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10007535/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10007535/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10007535/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10007535/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10007535.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">770</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8460</span> A Robust LS-SVM Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=J%C3%B3zsef%20Valyon">József Valyon</a>, <a href="https://publications.waset.org/search?q=G%C3%A1bor%20Horv%C3%A1th"> Gábor Horváth</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In comparison to the original SVM, which involves a quadratic programming task; LS–SVM simplifies the required computation, but unfortunately the sparseness of standard SVM is lost. Another problem is that LS-SVM is only optimal if the training samples are corrupted by Gaussian noise. In Least Squares SVM (LS–SVM), the nonlinear solution is obtained, by first mapping the input vector to a high dimensional kernel space in a nonlinear fashion, where the solution is calculated from a linear equation set. In this paper a geometric view of the kernel space is introduced, which enables us to develop a new formulation to achieve a sparse and robust estimate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Support%20Vector%20Machines" title="Support Vector Machines">Support Vector Machines</a>, <a href="https://publications.waset.org/search?q=Least%20Squares%20SupportVector%20Machines" title=" Least Squares SupportVector Machines"> Least Squares SupportVector Machines</a>, <a href="https://publications.waset.org/search?q=Regression" title=" Regression"> Regression</a>, <a href="https://publications.waset.org/search?q=Sparse%20approximation." title=" Sparse approximation."> Sparse approximation.</a> </p> <a href="https://publications.waset.org/7407/a-robust-ls-svm-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7407/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7407/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7407/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7407/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7407/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7407/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7407/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7407/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7407/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7407/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7407.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2063</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8459</span> Sparse Networks-Based Speedup Technique for Proteins Betweenness Centrality Computation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Razvan%20Bocu">Razvan Bocu</a>, <a href="https://publications.waset.org/search?q=Dr%20Sabin%20Tabirca"> Dr Sabin Tabirca</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of proteomics reached unexpected levels of interest, as a direct consequence of its discovered influence over some complex biological phenomena, such as problematic diseases like cancer. This paper presents the latest authors- achievements regarding the analysis of the networks of proteins (interactome networks), by computing more efficiently the betweenness centrality measure. The paper introduces the concept of betweenness centrality, and then describes how betweenness computation can help the interactome net- work analysis. Current sequential implementations for the between- ness computation do not perform satisfactory in terms of execution times. The paper-s main contribution is centered towards introducing a speedup technique for the betweenness computation, based on modified shortest path algorithms for sparse graphs. Three optimized generic algorithms for betweenness computation are described and implemented, and their performance tested against real biological data, which is part of the IntAct dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Betweenness%20centrality" title="Betweenness centrality">Betweenness centrality</a>, <a href="https://publications.waset.org/search?q=interactome%20networks" title=" interactome networks"> interactome networks</a>, <a href="https://publications.waset.org/search?q=protein-protein%20interactions" title=" protein-protein interactions"> protein-protein interactions</a>, <a href="https://publications.waset.org/search?q=sub-communities" title=" sub-communities"> sub-communities</a>, <a href="https://publications.waset.org/search?q=sparse%20networks" title=" sparse networks"> sparse networks</a>, <a href="https://publications.waset.org/search?q=speedup%20tech-nique" title=" speedup tech-nique"> speedup tech-nique</a>, <a href="https://publications.waset.org/search?q=IntAct." title=" IntAct."> IntAct.</a> </p> <a href="https://publications.waset.org/10230/sparse-networks-based-speedup-technique-for-proteins-betweenness-centrality-computation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10230/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10230/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10230/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10230/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10230/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10230/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10230/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10230/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10230/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10230/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10230.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1507</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8458</span> Information Filtering using Index Word Selection based on the Topics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Takeru%20YOKOI">Takeru YOKOI</a>, <a href="https://publications.waset.org/search?q=Hidekazu%20YANAGIMOTO"> Hidekazu YANAGIMOTO</a>, <a href="https://publications.waset.org/search?q=Sigeru%20OMATU"> Sigeru OMATU</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We have proposed an information filtering system using index word selection from a document set based on the topics included in a set of documents. This method narrows down the particularly characteristic words in a document set and the topics are obtained by Sparse Non-negative Matrix Factorization. In information filtering, a document is often represented with the vector in which the elements correspond to the weight of the index words, and the dimension of the vector becomes larger as the number of documents is increased. Therefore, it is possible that useless words as index words for the information filtering are included. In order to address the problem, the dimension needs to be reduced. Our proposal reduces the dimension by selecting index words based on the topics included in a document set. We have applied the Sparse Non-negative Matrix Factorization to the document set to obtain these topics. The filtering is carried out based on a centroid of the learning document set. The centroid is regarded as the user-s interest. In addition, the centroid is represented with a document vector whose elements consist of the weight of the selected index words. Using the English test collection MEDLINE, thus, we confirm the effectiveness of our proposal. Hence, our proposed selection can confirm the improvement of the recommendation accuracy from the other previous methods when selecting the appropriate number of index words. In addition, we discussed the selected index words by our proposal and we found our proposal was able to select the index words covered some minor topics included in the document set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Information%20Filtering" title="Information Filtering">Information Filtering</a>, <a href="https://publications.waset.org/search?q=Sparse%20NMF" title=" Sparse NMF"> Sparse NMF</a>, <a href="https://publications.waset.org/search?q=Index%20wordSelection" title=" Index wordSelection"> Index wordSelection</a>, <a href="https://publications.waset.org/search?q=User%20Profile" title=" User Profile"> User Profile</a>, <a href="https://publications.waset.org/search?q=Chi-squared%20Measure" title=" Chi-squared Measure"> Chi-squared Measure</a> </p> <a href="https://publications.waset.org/14748/information-filtering-using-index-word-selection-based-on-the-topics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14748/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14748/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14748/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14748/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14748/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14748/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14748/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14748/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14748/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14748/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14748.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1456</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8457</span> Performance Analysis and Optimization for Diagonal Sparse Matrix-Vector Multiplication on Machine Learning Unit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Qiuyu%20Dai">Qiuyu Dai</a>, <a href="https://publications.waset.org/search?q=Haochong%20Zhang"> Haochong Zhang</a>, <a href="https://publications.waset.org/search?q=Xiangrong%20Liu"> Xiangrong Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Efficient matrix-vector multiplication with diagonal sparse matrices is pivotal in a multitude of computational domains, ranging from scientific simulations to machine learning workloads. When encoded in the conventional Diagonal (DIA) format, these matrices often induce computational overheads due to extensive zero-padding and non-linear memory accesses, which can hamper the computational throughput, and elevate the usage of precious compute and memory resources beyond necessity. The ’DIA-Adaptive’ approach, a methodological enhancement introduced in this paper, confronts these challenges head-on by leveraging the advanced parallel instruction sets embedded within Machine Learning Units (MLUs). This research presents a thorough analysis of the DIA-Adaptive scheme’s efficacy in optimizing Sparse Matrix-Vector Multiplication (SpMV) operations. The scope of the evaluation extends to a variety of hardware architectures, examining the repercussions of distinct thread allocation strategies and cluster configurations across multiple storage formats. A dedicated computational kernel, intrinsic to the DIA-Adaptive approach, has been meticulously developed to synchronize with the nuanced performance characteristics of MLUs. Empirical results, derived from rigorous experimentation, reveal that the DIA-Adaptive methodology not only diminishes the performance bottlenecks associated with the DIA format but also exhibits pronounced enhancements in execution speed and resource utilization. The analysis delineates a marked improvement in parallelism, showcasing the DIA-Adaptive scheme’s ability to adeptly manage the interplay between storage formats, hardware capabilities, and algorithmic design. The findings suggest that this approach could set a precedent for accelerating SpMV tasks, thereby contributing significantly to the broader domain of high-performance computing and data-intensive applications.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Adaptive%20method" title="Adaptive method">Adaptive method</a>, <a href="https://publications.waset.org/search?q=DIA" title=" DIA"> DIA</a>, <a href="https://publications.waset.org/search?q=diagonal%20sparse%20matrices" title=" diagonal sparse matrices"> diagonal sparse matrices</a>, <a href="https://publications.waset.org/search?q=MLU" title=" MLU"> MLU</a>, <a href="https://publications.waset.org/search?q=sparse%20matrix-vector%20multiplication." title=" sparse matrix-vector multiplication."> sparse matrix-vector multiplication.</a> </p> <a href="https://publications.waset.org/10013375/performance-analysis-and-optimization-for-diagonal-sparse-matrix-vector-multiplication-on-machine-learning-unit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013375/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013375/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013375/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013375/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013375/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013375/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013375/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013375/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013375/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013375/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013375.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8456</span> A Fast HRRP Synthesis Algorithm with Sensing Dictionary in GTD Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.%20Fan">R. Fan</a>, <a href="https://publications.waset.org/search?q=Q.%20Wan"> Q. Wan</a>, <a href="https://publications.waset.org/search?q=H.%20Chen"> H. Chen</a>, <a href="https://publications.waset.org/search?q=Y.L.%20Liu"> Y.L. Liu</a>, <a href="https://publications.waset.org/search?q=Y.P.%20Liu"> Y.P. Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In the paper, a fast high-resolution range profile synthetic algorithm called orthogonal matching pursuit with sensing dictionary (OMP-SD) is proposed. It formulates the traditional HRRP synthetic to be a sparse approximation problem over redundant dictionary. As it employs a priori that the synthetic range profile (SRP) of targets are sparse, SRP can be accomplished even in presence of data lost. Besides, the computation complexity decreases from O(MNDK) flops for OMP to O(M(N + D)K) flops for OMP-SD by introducing sensing dictionary (SD). Simulation experiments illustrate its advantages both in additive white Gaussian noise (AWGN) and noiseless situation, respectively.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=GTD-based%20model" title="GTD-based model">GTD-based model</a>, <a href="https://publications.waset.org/search?q=HRRP" title=" HRRP"> HRRP</a>, <a href="https://publications.waset.org/search?q=orthogonal%20matching%20pursuit" title=" orthogonal matching pursuit"> orthogonal matching pursuit</a>, <a href="https://publications.waset.org/search?q=sensing%20dictionary." title=" sensing dictionary."> sensing dictionary.</a> </p> <a href="https://publications.waset.org/15081/a-fast-hrrp-synthesis-algorithm-with-sensing-dictionary-in-gtd-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15081/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15081/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15081/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15081/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15081/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15081/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15081/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15081/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15081/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15081/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15081.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1923</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8455</span> Auto Regressive Tree Modeling for Parametric Optimization in Fuzzy Logic Control System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Arshia%20Azam">Arshia Azam</a>, <a href="https://publications.waset.org/search?q=J.%20Amarnath"> J. Amarnath</a>, <a href="https://publications.waset.org/search?q=Ch.%20D.%20V.%20Paradesi%20Rao"> Ch. D. V. Paradesi Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The advantage of solving the complex nonlinear problems by utilizing fuzzy logic methodologies is that the experience or expert-s knowledge described as a fuzzy rule base can be directly embedded into the systems for dealing with the problems. The current limitation of appropriate and automated designing of fuzzy controllers are focused in this paper. The structure discovery and parameter adjustment of the Branched T-S fuzzy model is addressed by a hybrid technique of type constrained sparse tree algorithms. The simulation result for different system model is evaluated and the identification error is observed to be minimum. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fuzzy%20logic" title="Fuzzy logic">Fuzzy logic</a>, <a href="https://publications.waset.org/search?q=branch%20T-S%20fuzzy%20model" title=" branch T-S fuzzy model"> branch T-S fuzzy model</a>, <a href="https://publications.waset.org/search?q=tree%20modeling" title=" tree modeling"> tree modeling</a>, <a href="https://publications.waset.org/search?q=complex%20nonlinear%20system." title=" complex nonlinear system."> complex nonlinear system.</a> </p> <a href="https://publications.waset.org/6089/auto-regressive-tree-modeling-for-parametric-optimization-in-fuzzy-logic-control-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6089/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6089/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6089/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6089/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6089/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6089/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6089/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6089/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6089/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6089/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6089.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1389</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8454</span> Review and Experiments on SDMSCue</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ashraf%20Anwar">Ashraf Anwar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, I present a review on Sparse Distributed Memory for Small Cues (SDMSCue), a variant of Sparse Distributed Memory (SDM) that is capable of handling small cues. I then conduct and show some cognitive experiments on SDMSCue to test its cognitive soundness compared to SDM. Small cues refer to input cues that are presented to memory for reading associations; but have many missing parts or fields from them. The original SDM failed to handle such a problem. SDMSCue handles and overcomes this pitfall. The main idea in SDMSCue; is the repeated projection of the semantic space on smaller subspaces; that are selected based on the input cue length and pattern. This process allows for Read/Write operations using an input cue that is missing a large portion. SDMSCue is augmented with the use of genetic algorithms for memory allocation and initialization. I claim that SDM functionality is a subset of SDMSCue functionality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20intelligence" title="Artificial intelligence">Artificial intelligence</a>, <a href="https://publications.waset.org/search?q=recall" title=" recall"> recall</a>, <a href="https://publications.waset.org/search?q=recognition" title=" recognition"> recognition</a>, <a href="https://publications.waset.org/search?q=SDM" title=" SDM"> SDM</a>, <a href="https://publications.waset.org/search?q=SDMSCue." title="SDMSCue.">SDMSCue.</a> </p> <a href="https://publications.waset.org/8312/review-and-experiments-on-sdmscue" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8312/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8312/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8312/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8312/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8312/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8312/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8312/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8312/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8312/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8312/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8312.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1373</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8453</span> Performance Analysis of Learning Automata-Based Routing Algorithms in Sparse Graphs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Z.Farhadpour">Z.Farhadpour</a>, <a href="https://publications.waset.org/search?q=Mohammad.R.Meybodi"> Mohammad.R.Meybodi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A number of routing algorithms based on learning automata technique have been proposed for communication networks. How ever, there has been little work on the effects of variation of graph scarcity on the performance of these algorithms. In this paper, a comprehensive study is launched to investigate the performance of LASPA, the first learning automata based solution to the dynamic shortest path routing, across different graph structures with varying scarcities. The sensitivity of three main performance parameters of the algorithm, being average number of processed nodes, scanned edges and average time per update, to variation in graph scarcity is reported. Simulation results indicate that the LASPA algorithm can adapt well to the scarcity variation in graph structure and gives much better outputs than the existing dynamic and fixed algorithms in terms of performance criteria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Learning%20automata" title="Learning automata">Learning automata</a>, <a href="https://publications.waset.org/search?q=routing" title=" routing"> routing</a>, <a href="https://publications.waset.org/search?q=algorithm" title=" algorithm"> algorithm</a>, <a href="https://publications.waset.org/search?q=sparse%0Agraph" title=" sparse graph"> sparse graph</a> </p> <a href="https://publications.waset.org/863/performance-analysis-of-learning-automata-based-routing-algorithms-in-sparse-graphs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/863/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/863/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/863/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/863/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/863/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/863/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/863/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/863/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/863/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/863/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/863.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1357</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8452</span> High Performance Computing Using Out-of- Core Sparse Direct Solvers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mandhapati%20P.%20Raju">Mandhapati P. Raju</a>, <a href="https://publications.waset.org/search?q=Siddhartha%20Khaitan"> Siddhartha Khaitan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In-core memory requirement is a bottleneck in solving large three dimensional Navier-Stokes finite element problem formulations using sparse direct solvers. Out-of-core solution strategy is a viable alternative to reduce the in-core memory requirements while solving large scale problems. This study evaluates the performance of various out-of-core sequential solvers based on multifrontal or supernodal techniques in the context of finite element formulations for three dimensional problems on a Windows platform. Here three different solvers, HSL_MA78, MUMPS and PARDISO are compared. The performance of these solvers is evaluated on a 64-bit machine with 16GB RAM for finite element formulation of flow through a rectangular channel. It is observed that using out-of-core PARDISO solver, relatively large problems can be solved. The implementation of Newton and modified Newton's iteration is also discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Out-of-core" title="Out-of-core">Out-of-core</a>, <a href="https://publications.waset.org/search?q=PARDISO" title=" PARDISO"> PARDISO</a>, <a href="https://publications.waset.org/search?q=MUMPS" title=" MUMPS"> MUMPS</a>, <a href="https://publications.waset.org/search?q=Newton." title=" Newton."> Newton.</a> </p> <a href="https://publications.waset.org/4908/high-performance-computing-using-out-of-core-sparse-direct-solvers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4908/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4908/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4908/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4908/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4908/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4908/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4908/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4908/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4908/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4908/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2144</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8451</span> Sparse Coding Based Classification of Electrocardiography Signals Using Data-Driven Complete Dictionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Fuad%20Noman">Fuad Noman</a>, <a href="https://publications.waset.org/search?q=Sh-Hussain%20Salleh"> Sh-Hussain Salleh</a>, <a href="https://publications.waset.org/search?q=Chee-Ming%20Ting"> Chee-Ming Ting</a>, <a href="https://publications.waset.org/search?q=Hadri%20Hussain"> Hadri Hussain</a>, <a href="https://publications.waset.org/search?q=Syed%20Rasul"> Syed Rasul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Electrocardiogram" title="Electrocardiogram">Electrocardiogram</a>, <a href="https://publications.waset.org/search?q=dictionary%20learning" title=" dictionary learning"> dictionary learning</a>, <a href="https://publications.waset.org/search?q=sparse%20coding" title=" sparse coding"> sparse coding</a>, <a href="https://publications.waset.org/search?q=classification." title=" classification."> classification.</a> </p> <a href="https://publications.waset.org/10005163/sparse-coding-based-classification-of-electrocardiography-signals-using-data-driven-complete-dictionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005163/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005163/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005163/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005163/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005163/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005163/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005163/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005163/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005163/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005163/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005163.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2094</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8450</span> Iterative Image Reconstruction for Sparse-View Computed Tomography via Total Variation Regularization and Dictionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=XianYu%20Zhao">XianYu Zhao</a>, <a href="https://publications.waset.org/search?q=JinXu%20Guo"> JinXu Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, low-dose computed tomography (CT) has become highly desirable due to increasing attention to the potential risks of excessive radiation. For low-dose CT imaging, ensuring image quality while reducing radiation dose is a major challenge. To facilitate low-dose CT imaging, we propose an improved statistical iterative reconstruction scheme based on the Penalized Weighted Least Squares (PWLS) standard combined with total variation (TV) minimization and sparse dictionary learning (DL) to improve reconstruction performance. We call this method "PWLS-TV-DL". In order to evaluate the PWLS-TV-DL method, we performed experiments on digital phantoms and physical phantoms, respectively. The experimental results show that our method is in image quality and calculation. The efficiency is superior to other methods, which confirms the potential of its low-dose CT imaging. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Low%20dose%20computed%20tomography" title="Low dose computed tomography">Low dose computed tomography</a>, <a href="https://publications.waset.org/search?q=penalized%20weighted%20least%20squares" title=" penalized weighted least squares"> penalized weighted least squares</a>, <a href="https://publications.waset.org/search?q=total%20variation" title=" total variation"> total variation</a>, <a href="https://publications.waset.org/search?q=dictionary%20learning." title=" dictionary learning."> dictionary learning.</a> </p> <a href="https://publications.waset.org/10010269/iterative-image-reconstruction-for-sparse-view-computed-tomography-via-total-variation-regularization-and-dictionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10010269/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10010269/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10010269/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10010269/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10010269/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10010269/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10010269/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10010269/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10010269/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10010269/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10010269.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">834</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8449</span> A Hybrid Recommender System based on Collaborative Filtering and Cloud Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Chein-Shung%20Hwang">Chein-Shung Hwang</a>, <a href="https://publications.waset.org/search?q=Ruei-Siang%20Fong"> Ruei-Siang Fong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> User-based Collaborative filtering (CF), one of the most prevailing and efficient recommendation techniques, provides personalized recommendations to users based on the opinions of other users. Although the CF technique has been successfully applied in various applications, it suffers from serious sparsity problems. The cloud-model approach addresses the sparsity problems by constructing the user-s global preference represented by a cloud eigenvector. The user-based CF approach works well with dense datasets while the cloud-model CF approach has a greater performance when the dataset is sparse. In this paper, we present a hybrid approach that integrates the predictions from both the user-based CF and the cloud-model CF approaches. The experimental results show that the proposed hybrid approach can ameliorate the sparsity problem and provide an improved prediction quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cloud%20model" title="Cloud model">Cloud model</a>, <a href="https://publications.waset.org/search?q=Collaborative%20filtering" title=" Collaborative filtering"> Collaborative filtering</a>, <a href="https://publications.waset.org/search?q=Hybridrecommender%20system" title=" Hybridrecommender system"> Hybridrecommender system</a> </p> <a href="https://publications.waset.org/7682/a-hybrid-recommender-system-based-on-collaborative-filtering-and-cloud-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7682/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7682/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a 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